Driver assistance systems are systems that generally help a driver in a vehicle during the driving process. Some examples of driver assistance systems may include, but are not limited to in-vehicle navigation systems, Adaptive Cruise Control (ACC) systems, lane departure warning systems, collision avoidance systems, automatic parking systems, and blind spot detection systems. Driver assistance systems may be used to increase vehicle and road safety.
Modern vehicle systems, such as, but not limited to, driver assistance systems, rely on computer vision-based pedestrian detection. In vision-based detection systems, sensors may be equipped in vehicles to collect data from surroundings, and decision may be made based on sensory data. Typical sensors for detecting pedestrians may be cameras that capture images of surroundings. Pedestrians may be partially occluded by objects, such as cars, trees, shrubbery, signs, among others, in a driving scene. Accordingly, vehicle systems may use computer vision techniques, for example, part-based models trained on upper-body pedestrian images, to detect partially occluded pedestrians. However, an image-based detector trained on images of parts of an object may have lower precision than that of an image-based detector trained on images of a whole object. Thus, there is a need in the art for a partially occluded object detection system that also verifies whether a detection of a partially occluded object is accurate, so as to reduce false positive results.